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http://dx.doi.org/10.13106/jafeb.2022.vol9.no7.0001

Supremacy of Realized Variance MIDAS Regression in Volatility Forecasting of Mutual Funds: Empirical Evidence From Malaysia  

WAN, Cheong Kin (Department of Business, Faculty of Business, Economics & Accounting, HELP University)
CHOO, Wei Chong (Department of Management, School of Business and Economics, Universiti Putra Malaysia, Laboratory of Computational Statistics and Operations Research, Institute for Mathematical Research, Universiti Putra Malaysia)
HO, Jen Sim (School of Business and Economics, Universiti Putra Malaysia)
ZHANG, Yuruixian (School of Business and Economics, Universiti Putra Malaysia)
Publication Information
The Journal of Asian Finance, Economics and Business / v.9, no.7, 2022 , pp. 1-15 More about this Journal
Abstract
Combining the strength of both Mixed Data Sampling (MIDAS) Regression and realized variance measures, this paper seeks to investigate two objectives: (1) evaluate the post-sample performance of the proposed weekly Realized Variance-MIDAS (RVar-MIDAS) in one-week ahead volatility forecasting against the established Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model and the less explored but robust STES (Smooth Transition Exponential Smoothing) methods. (2) comparing forecast error performance between realized variance and squared residuals measures as a proxy for actual volatility. Data of seven private equity mutual fund indices (generated from 57 individual funds) from two different time periods (with and without financial crisis) are applied to 21 models. Robustness of the post-sample volatility forecasting of all models is validated by the Model Confidence Set (MCS) Procedures and revealed: (1) The weekly RVar-MIDAS model emerged as the best model, outperformed the robust DAILY-STES methods, and the weekly DAILY-GARCH models, particularly during a volatile period. (2) models with realized variance measured in estimation and as a proxy for actual volatility outperformed those using squared residual. This study contributes an empirical approach to one-week ahead volatility forecasting of mutual funds return, which is less explored in past literature on financial volatility forecasting compared to stocks volatility.
Keywords
Mixed Data Sampling; Realized Variance; Volatility Forecasting; Smooth Transition Exponential Smoothing; Mutual Funds;
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1 Sahadudheen, I. (2015). An exponential GARCH approach to the effect of the impulsiveness of the euro on the Indian stock market. Journal of Asian Finance, Economics, and Business, 2(3), 17-22. https://doi.org/10.13106/jafeb.2015.vol2.no3.17   DOI
2 Jiang, Y., Guo, Y., & Zhang, Y. (2017). Forecasting China's GDP growth using dynamic factors and mixed-frequency data. Economic Modelling, 66(11), 132-138. https://doi.org/10.1016/j.econmod.2017.06.005   DOI
3 Kors, M., & Karan, M. B. (2021). Stock exchange volatility forecasting under market stress with MIDAS regression. International Journal of Finance and Economics, 1, 1-12. https://doi.org/10.1002/ijfe.2421   DOI
4 Li, X., Shang, W., Wang, S., & Ma, J. (2015). A MIDAS modeling framework for Chinese inflation index forecast incorporating Google search data. Electronic Commerce Research and Applications, 14(2), 112-125. https://doi.org/10.1016/j.elerap.2015.01.001   DOI
5 Mandelbrot, B. (1963). The variation of certain speculative prices. Journal of Business, 36(4), 394. https://doi.org/10.1086/294632   DOI
6 Mcaleer, M. J., & Medeiros, M. C. (2008). Realized volatility: A review. Econometric Reviews, 27(1-3), 10-45. https://doi.org/10.1080/07474930701853509   DOI
7 Nelson, D. B. (1991). Conditional heteroscedasticity in asset returns: A new approach. Econometrica, 59(2), 347-370. https://doi.org/10.2307/2938260   DOI
8 Tsui, A. K., Xu, C. Y., & Zhang, Z. (2018). Macroeconomic forecasting with mixed data sampling frequencies: Evidence from a small open economy. Journal of Forecasting, 37(6), 666-675. https://doi.org/10.1002/for.2528   DOI
9 Wan, C. K., Choo, W. C., Nassir, A. M., Habibullah, M. S., & Yusop, Z. (2021). Volatility forecasting performance of smooth transition exponential smoothing method: Evidence from mutual fund indices in Malaysia. Asian Economic and Financial Review, 11(10), 829-859. https://doi.org/10.18488/journal.aefr.2021.1110.829.859   DOI
10 Xu, Q., Chen, L., Jiang, C., & Yu, K. (2020). Mixed data sampling expertise regression with applications to measuring financial risk. Economic Modelling, 91, 469-486. https://doi.org/10.1016/j.econmod.2020.06.018   DOI
11 Engle, R. F. (1982). Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econometrica, 50(4), 987-1007. https://doi.org/10.2307/1912773   DOI
12 Celik, S., & Ergin, H. (2014). Volatility forecasting using high-frequency data: Evidence from stock markets. Economic Modelling, 36(1), 176-190. https://doi.org/10.1016/j.econmod.2013.09.038   DOI
13 Choo, W. C., Ahmad, M. I., & Abdullah, M. Y. (1999). Performance of GARCH models in forecasting stock market volatility. Journal of Forecasting, 18(5), 333-343. https://doi.org/10.1002/(SICI)1099-131X(199909)18:5<333::AIDFOR742>3.0.CO;2-K   DOI
14 Chou, R. Y. (1988). Volatility persistence and stock valuations: Some empirical evidence using GARCH. Journal of Applied Econometrics, 3(4), 279-294. https://doi.org/10.1002/jae.3950030404   DOI
15 Glosten, L. R., Jagannathan, R., & Runkle, D. E. (1993). On the relation between the expected value and the volatility of the nominal excess return on stocks. Journal of Finance, 48(5), 1779-1801. https://doi.org/10.1111/j.1540-6261.1993.tb05128.x   DOI
16 Gooi, L. M., Choo, W. C., Nassir, A. M. D., & Ng, S. I. (2018). Volatility forecasting of real estate stock in Malaysia with smooth transition exponential smoothing. International Journal of Economics and Management, 12(2), 731-745.
17 Gorgi, P., Koopman, S. J., & Li, M. (2019). Forecasting economic time series using score-driven dynamic models with mixed-data sampling. International Journal of Forecasting, 35(4), 1735-1747. https://doi.org/10.1016/j.ijforecast.2018.11.005   DOI
18 Xiong, X., Liu, J., & Liu, Z. (2020). Can economic policy uncertainty predict financial stress? A MIDAS approach. Applied Economics Letters, 27, 1-8. https://doi.org/https. https://doi.org/10.1080/13504851.2020.1854664   DOI
19 McMillan, D. G., & Speight, A. E. H. (2004). Daily volatility forecasts: Reassessing the performance of GARCH models. Journal of Forecasting, 23(6), 449-460. https://doi.org/10.1002/for.926   DOI
20 Liu, M., Taylor, J. W., & Choo, W. C. (2020). Further empirical evidence on the forecasting of volatility with smooth transition exponential smoothing. Economic Modelling, 93(12), 651-659. https://doi.org/10.1016/j.econmod.2020.02.021   DOI
21 Choo, W. C. (2008). Volatility forecasting with exponential weighting, smooth transition, and robust methods. Oxford: The University of Oxford.
22 Rahmi, M., Azma, N., Muttaqin, A. A., Jazil, T., & Rahman, M. (2016). Risk volatility measurement: Evidence from Indonesian stock market. Journal of Asian Finance, Economics, and Business, 3(3), 57-65. https://doi.org/10.13106/jafeb.2016.vol3.no3.57   DOI
23 Taylor, J. W. (2004b). Volatility forecasting with smooth transition exponential smoothing. International Journal of Forecasting, 20(2), 273-286. https://doi.org/10.1016/j.ijforecast.2003.09.010   DOI
24 Ghysels, E., Santa-Clara, P., & Valkanov, R. (2004). The MIDAS Touch: Mixed data sampling regression models.
25 Taylor, J. W. (2004a). Smooth transition exponential smoothing. Journal of Forecasting, 23(6), 385-404. https://doi.org/10.1002/for.918   DOI
26 Nguyen, C. T., & Nguyen, M. H. (2019). Modeling stock price volatility: Empirical evidence from the Ho Chi Minh City stock exchange in Vietnam. Journal of Asian Finance, Economics, and Business, 6(3), 19-26. https://doi.org/10.13106/jafeb.2019.vol6.no3.19   DOI
27 Poon, S., & Taylor, S. J. (1992). Stock returns and volatility: An empirical study of the UK stock market. Journal of Banking and Finance, 16(1), 37-59. https://doi.org/10.1016/0378-4266(92)90077-D   DOI
28 Sharma, P., & Vipul, M. (2016). Forecasting stock market volatility using Realized GARCH model: International evidence. Quarterly Review of Economics and Finance, 59(2), 222-230. https://doi.org/10.1016/j.qref.2015.07.005   DOI
29 Barndorff-Nielsen, O. E., & Shephard, N. (2002). Econometric analysis of realized volatility and its use in estimating stochastic volatility models. Journal of the Royal Statistical Society: Series B, 64(2), 253-280. https://doi.org/10.1111/1467-9868.00336   DOI
30 Parasuraman, N. R., & Ramudu, P. J. (2014). Price weighted vs. Value weighted index: A comparative analysis and impact on index-based portfolio performance. New York: Tata McGrawHill.
31 Breitung, J., & Roling, C. (2015). Forecasting inflation rates using daily data: A nonparametric MIDAS Approach. Journal of Forecasting, 34(7), 588-603. https://doi.org/10.1002/for.2361   DOI
32 Corielli, F., & Marcellino, M. (2006). Factor-based index tracking. Journal of Banking and Finance, 30(8), 2215-2233. https://doi.org/10.1016/j.jbankfin.2005.07.012   DOI
33 Gonzalez-Rivera, G. (1998). Smooth-transition GARCH models. Studies in Nonlinear Dynamics and Econometrics, 3(2), 61-78. https://doi.org/10.2202/1558-3708.1041   DOI
34 Hansen, P. R., Huang, Z., & Shek, H. H. (2012). Realized GARCH: A joint model for returns and realized measures of volatility. Journal of Applied Econometrics, 27(6), 877-906. https://doi.org/10.1002/jae.1234   DOI
35 Koopman, S. J., Jungbacker, B., & Hol, E. (2005). Forecasting daily variability of the S&P 100 stock index using historical, realized, and implied volatility measurements. Journal of Empirical Finance, 12(3), 445-475. https://doi.org/10.1016/j.jempfin.2004.04.009   DOI
36 Franses, P. H., & Ghijsels, H. (1999). Additive outliers, GARCH, and forecasting volatility. International Journal of Forecasting, 15(1), 1-9. https://doi.org/10.1016/S0169-2070(98)00053-3   DOI
37 Hansen, P. R., & Lunde, A. (2005). A forecast comparison of volatility models: Does anything beat a GARCH((1, 1))? Journal of Applied Econometrics, 20(7), 873-889. https://doi.org/10.1002/jae.800   DOI
38 Hansen, P. R., Lunde, A., & Nason, J. N. (2011). The model confidence set. Econometrica, 79(2), 453-497. https://doi.org/10.3982/ECTA5771   DOI
39 Golder, U., Rumaly, N., Shahriar, A. H. M., & Alam, M. J. (2022). The impact of COVID-19 on the volatility of Bangladeshi stock market: Evidence from GJR-GARCH model. Journal of Asian Finance, Economics, and Business, 9(4), 29-38. https://doi.org/10.13106/jafeb.2022.vol9.no4.0029   DOI
40 Poon, S.H., & Granger, C. W. J. (2003). Forecasting volatility in financial markets: A review. Journal of Economic Literature, 41(2), 478-539. https://doi.org/10.1257/002205103765762743   DOI
41 Andersen, T. G., & Bollerslev, T. (1998). Answering the skeptics: Yes, standard volatility models do provide accurate forecasts. International Economic Review, 39(4), 1-22. https://doi.org/10.2307/2527343   DOI
42 Alper, C. E., Fendoglu, S., & Saltoglu, B. (2012). MIDAS volatility forecast performance under market stress: Evidence from emerging stock markets. Economics Letters, 117(2), 528-532. https://doi.org/10.1016/j.econlet.2012.05.037   DOI
43 Andersen, T. G., Bollerslev, T., Diebold, F. X., & Labys, P. (2003). Modeling and forecasting realized volatility. Econometrica, 71(2), 579-625. https://doi.org/10.1111/1468-0262.00418   DOI
44 Bollerslev, T. (1986). Generalized autoregressive conditional heteroskedasticity. Journal of Econometrics, 31(3), 307-327. https://doi.org/10.1016/0304-4076(86)90063-1   DOI
45 Brooks, C. (1998). Predicting stock index volatility: Can market volume help? Journal of Forecasting, 17(1), 59-80. https://doi.org/10.1002/(SICI)1099-131X(199801)17:1<59::AIDFOR676>3.0.CO;2-H   DOI
46 Huang, Z., Liu, H., & Wang, T. (2016). Modeling long memory volatility using realized measures of volatility: A realized HAR GARCH model. Economic Modelling, 52, 812-821. https://doi.org/10.1016/j.econmod.2015.10.018   DOI